Characterizing drug mentions in COVID-19 Twitter Chatter release_pxafaaq6ojblzhlovx2h3pe6rm

by Ramya Tekumalla, Juan M. Banda

Released as a article .

2020  

Abstract

Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain the virus. Although there is no specific antiviral treatment recommended for COVID-19, there are several drugs that can potentially help with symptoms. In this work, we mined a large twitter dataset of 424 million tweets of COVID-19 chatter to identify discourse around drug mentions. While seemingly a straightforward task, due to the informal nature of language use in Twitter, we demonstrate the need of machine learning alongside traditional automated methods to aid in this task. By applying these complementary methods, we are able to recover almost 15% additional data, making misspelling handling a needed task as a pre-processing step when dealing with social media data.
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Type  article
Stage   submitted
Date   2020-10-09
Version   v2
Language   en ?
arXiv  2007.10276v2
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